Import Libraries

# importing all the necessary libraries
library(tsibble)
## 
## Attaching package: 'tsibble'
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, union
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.1.0     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

Import dataset

# CREATE THE DATAFRAME
data <- read_csv('Covid_Age.csv')
## Rows: 46 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl  (2): Cases, Age
## date (1): Week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data <- data_frame(data)
## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
head(data)
## # A tibble: 6 × 3
##   Week       Cases   Age
##   <date>     <dbl> <dbl>
## 1 2021-01-03     7    88
## 2 2021-01-10     6    83
## 3 2021-01-17     6    83
## 4 2021-01-24     5    90
## 5 2021-01-31     5    85
## 6 2021-02-07     5    80

Check for Null Values

#CHECK FOR NULL VALUES IN THE DF
is.null(data) 
## [1] FALSE
#Number of Cases each week
fig <- plot_ly(data, x = ~Week, y = ~Cases, type = 'scatter', mode='lines+markers')
fig
#Age v/s Number of cases
fig_1 <- plot_ly(data, x = ~Age, y = ~Cases, type = 'bar')
fig_1

Conclusion